Eda_Final_Peter_2
library(tidyverse)## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.6 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.4 ✓ stringr 1.4.0
## ✓ readr 2.0.2 ✓ forcats 0.5.1## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(ggplot2)
library(ggpubr)data <- read_csv("Energy Census and Economic Data US 2010-2014.csv")## Rows: 52 Columns: 192## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): StateCodes, State
## dbl (190): Region, Division, Coast, Great Lakes, TotalC2010, TotalC2011, Tot...##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.head(data)## # A tibble: 6 × 192
## StateCodes State Region Division Coast `Great Lakes` TotalC2010 TotalC2011
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 AL Alabama 3 6 1 0 1931522 1905207
## 2 AK Alaska 4 9 1 0 653221 653637
## 3 AZ Arizona 4 8 0 0 1383531 1424944
## 4 AR Arkansas 3 7 0 0 1120632 1122544
## 5 CA Californ… 4 9 1 0 7760629 7777115
## 6 CO Colorado 4 8 0 0 1513547 1470445
## # … with 184 more variables: TotalC2012 <dbl>, TotalC2013 <dbl>,
## # TotalC2014 <dbl>, TotalP2010 <dbl>, TotalP2011 <dbl>, TotalP2012 <dbl>,
## # TotalP2013 <dbl>, TotalP2014 <dbl>, TotalE2010 <dbl>, TotalE2011 <dbl>,
## # TotalE2012 <dbl>, TotalE2013 <dbl>, TotalE2014 <dbl>, TotalPrice2010 <dbl>,
## # TotalPrice2011 <dbl>, TotalPrice2012 <dbl>, TotalPrice2013 <dbl>,
## # TotalPrice2014 <dbl>, TotalC10-11 <dbl>, TotalC11-12 <dbl>,
## # TotalC12-13 <dbl>, TotalC13-14 <dbl>, TotalP10-11 <dbl>, …str(data)## spec_tbl_df [52 × 192] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
## $ StateCodes : chr [1:52] "AL" "AK" "AZ" "AR" ...
## $ State : chr [1:52] "Alabama" "Alaska" "Arizona" "Arkansas" ...
## $ Region : num [1:52] 3 4 4 3 4 4 1 3 3 3 ...
## $ Division : num [1:52] 6 9 8 7 9 8 1 5 5 5 ...
## $ Coast : num [1:52] 1 1 0 0 1 0 1 1 1 1 ...
## $ Great Lakes : num [1:52] 0 0 0 0 0 0 0 0 0 0 ...
## $ TotalC2010 : num [1:52] 1931522 653221 1383531 1120632 7760629 ...
## $ TotalC2011 : num [1:52] 1905207 653637 1424944 1122544 7777115 ...
## $ TotalC2012 : num [1:52] 1879716 649341 1395839 1067642 7564063 ...
## $ TotalC2013 : num [1:52] 1919365 621107 1414383 1096438 7665241 ...
## $ TotalC2014 : num [1:52] 1958221 603119 1422590 1114409 7620082 ...
## $ TotalP2010 : num [1:52] 1419613 1738207 580948 1247709 2532205 ...
## $ TotalP2011 : num [1:52] 1400108 1641980 617956 1391190 2634789 ...
## $ TotalP2012 : num [1:52] 1433370 1563102 598039 1472778 2334863 ...
## $ TotalP2013 : num [1:52] 1463647 1513859 594994 1432074 2390424 ...
## $ TotalP2014 : num [1:52] 1353725 1475129 635050 1454325 2413494 ...
## $ TotalE2010 : num [1:52] 21630 6474 19373 12269 117090 ...
## $ TotalE2011 : num [1:52] 24448 8050 22629 14179 135098 ...
## $ TotalE2012 : num [1:52] 24193 7884 22872 13756 135932 ...
## $ TotalE2013 : num [1:52] 24127 7282 22841 14102 137051 ...
## $ TotalE2014 : num [1:52] 24146 6891 22610 13885 137720 ...
## $ TotalPrice2010 : num [1:52] 17.8 20.1 22.2 16.9 21 ...
## $ TotalPrice2011 : num [1:52] 20.1 24.9 25.6 19.7 24.4 ...
## $ TotalPrice2012 : num [1:52] 20 25.1 26.4 20 25 ...
## $ TotalPrice2013 : num [1:52] 19 24.6 26.1 19.5 24.9 ...
## $ TotalPrice2014 : num [1:52] 18.6 24.4 25.9 18.9 25.3 ...
## $ TotalC10-11 : num [1:52] 98.6 100.1 103 100.2 100.2 ...
## $ TotalC11-12 : num [1:52] 98.7 99.3 98 95.1 97.3 ...
## $ TotalC12-13 : num [1:52] 102.1 95.7 101.3 102.7 101.3 ...
## $ TotalC13-14 : num [1:52] 102 97.1 100.6 101.6 99.4 ...
## $ TotalP10-11 : num [1:52] 98.6 94.5 106.4 111.5 104.1 ...
## $ TotalP11-12 : num [1:52] 102.4 95.2 96.8 105.9 88.6 ...
## $ TotalP12-13 : num [1:52] 102.1 96.8 99.5 97.2 102.4 ...
## $ TotalP13-14 : num [1:52] 92.5 97.4 106.7 101.6 101 ...
## $ TotalE10-11 : num [1:52] 113 124 117 116 115 ...
## $ TotalE11-12 : num [1:52] 99 97.9 101.1 97 100.6 ...
## $ TotalE12-13 : num [1:52] 99.7 92.4 99.9 102.5 100.8 ...
## $ TotalE13-14 : num [1:52] 100.1 94.6 99 98.5 100.5 ...
## $ TotalPrice10-11 : num [1:52] 113 124 115 116 116 ...
## $ TotalPrice11-12 : num [1:52] 99.3 100.7 103.1 101.6 102.5 ...
## $ TotalPrice12-13 : num [1:52] 95.3 98 98.6 97.8 99.5 ...
## $ TotalPrice13-14 : num [1:52] 98 99.3 99.6 96.6 101.9 ...
## $ BiomassC2010 : num [1:52] 169088 4178 29289 94865 280124 ...
## $ BiomassC2011 : num [1:52] 179611 4247 28565 97160 282581 ...
## $ BiomassC2012 : num [1:52] 181878 4032 27267 96248 280963 ...
## $ BiomassC2013 : num [1:52] 194432 5168 26492 95356 294412 ...
## $ BiomassC2014 : num [1:52] 186649 5476 31481 95963 298473 ...
## $ CoalC2010 : num [1:52] 718684 14548 457909 293689 54972 ...
## $ CoalC2011 : num [1:52] 651032 15481 459909 306119 55264 ...
## $ CoalC2012 : num [1:52] 547004 15521 420570 296732 43832 ...
## $ CoalC2013 : num [1:52] 565051 14819 454865 327099 38151 ...
## $ CoalC2014 : num [1:52] 575912 18225 447849 339214 39486 ...
## $ CoalP2010 : num [1:52] 493094 33556 167930 718 0 ...
## $ CoalP2011 : num [1:52] 468671 33524 174841 2985 0 ...
## $ CoalP2012 : num [1:52] 488084 31332 161374 2077 0 ...
## $ CoalP2013 : num [1:52] 469162 24917 163691 1433 0 ...
## $ CoalP2014 : num [1:52] 414366 22944 173337 1864 0 ...
## $ CoalE2010 : num [1:52] 2135.6 49.9 829.1 510.6 161.4 ...
## $ CoalE2011 : num [1:52] 2009.7 59.6 917.2 592.2 173.1 ...
## $ CoalE2012 : num [1:52] 1809 63 880 673 134 ...
## $ CoalE2013 : num [1:52] 1731.6 72.6 946.8 789.4 129.5 ...
## $ CoalE2014 : num [1:52] 1677.3 88.8 944.6 820.9 135.3 ...
## $ CoalPrice2010 : num [1:52] 2.97 3.43 1.81 1.74 2.94 1.59 3.45 3.35 3.48 3.9 ...
## $ CoalPrice2011 : num [1:52] 3.09 3.85 1.99 1.93 3.13 1.73 3.68 3.41 3.55 3.78 ...
## $ CoalPrice2012 : num [1:52] 3.31 4.06 2.09 2.27 3.05 1.86 3.59 3.35 3.51 3.51 ...
## $ CoalPrice2013 : num [1:52] 3.06 4.9 2.08 2.41 3.39 1.93 4.21 3.2 3.44 3.22 ...
## $ CoalPrice2014 : num [1:52] 2.91 4.87 2.11 2.42 3.43 1.95 4.27 3.08 3.33 3.15 ...
## $ ElecC2010 : num [1:52] 310023 21315 248506 164439 882107 ...
## $ ElecC2011 : num [1:52] 303652 21562 255708 163530 893745 ...
## $ ElecC2012 : num [1:52] 294055 21893 256116 159885 885544 ...
## $ ElecC2013 : num [1:52] 299751 21387 258159 159283 891666 ...
## $ ElecC2014 : num [1:52] 308765 21034 260328 160638 895939 ...
## $ ElecE2010 : num [1:52] 7833 912 7059 3393 33382 ...
## $ ElecE2011 : num [1:52] 7846 1005 7279 3447 33919 ...
## $ ElecE2012 : num [1:52] 7666 1035 7361 3456 34852 ...
## $ ElecE2013 : num [1:52] 7901 1005 7669 3687 37028 ...
## $ ElecE2014 : num [1:52] 8363 1056 7764 3704 39424 ...
## $ ElecPrice2010 : num [1:52] 26.4 43.3 28.4 21.6 38.2 ...
## $ ElecPrice2011 : num [1:52] 27.1 47.1 28.5 22 38.4 ...
## $ ElecPrice2012 : num [1:52] 27.3 47.8 28.7 22.6 39.8 ...
## $ ElecPrice2013 : num [1:52] 26.5 48.4 29.7 23.3 42 ...
## $ ElecPrice2014 : num [1:52] 27.2 51.3 29.8 23.2 44.5 ...
## $ FossFuelC2010 : num [1:52] 1785688 634777 1292346 909827 5741492 ...
## $ FossFuelC2011 : num [1:52] 1783839 635981 1253992 932878 5550201 ...
## $ FossFuelC2012 : num [1:52] 1740315 629768 1248032 918078 5687343 ...
## $ FossFuelC2013 : num [1:52] 1689491 600660 1289119 936796 5755210 ...
## $ FossFuelC2014 : num [1:52] 1724891 581369 1256577 939788 5702418 ...
## $ GeoC2010 : num [1:52] 138 153 339 773 124981 ...
## $ GeoC2011 : num [1:52] 135 214 345 750 124092 ...
## $ GeoC2012 : num [1:52] 141 186 345 808 121269 ...
## $ GeoC2013 : num [1:52] 141 186 345 808 119556 ...
## $ GeoC2014 : num [1:52] 141 186 345 808 117226 ...
## $ GeoP2010 : num [1:52] 0 0 0 0 12600 0 0 0 0 0 ...
## $ GeoP2011 : num [1:52] 0 0 0 0 12552 ...
## $ GeoP2012 : num [1:52] 0 0 0 0 12519 ...
## $ GeoP2013 : num [1:52] 0 0 0 0 12307 ...
## $ GeoP2014 : num [1:52] 0 0 0 0 12102 ...
## $ HydroC2010 : num [1:52] 84919 13982 64606 35697 326152 ...
## $ HydroC2011 : num [1:52] 86313 13066 89135 28738 413488 ...
## [list output truncated]
## - attr(*, "spec")=
## .. cols(
## .. StateCodes = col_character(),
## .. State = col_character(),
## .. Region = col_double(),
## .. Division = col_double(),
## .. Coast = col_double(),
## .. `Great Lakes` = col_double(),
## .. TotalC2010 = col_double(),
## .. TotalC2011 = col_double(),
## .. TotalC2012 = col_double(),
## .. TotalC2013 = col_double(),
## .. TotalC2014 = col_double(),
## .. TotalP2010 = col_double(),
## .. TotalP2011 = col_double(),
## .. TotalP2012 = col_double(),
## .. TotalP2013 = col_double(),
## .. TotalP2014 = col_double(),
## .. TotalE2010 = col_double(),
## .. TotalE2011 = col_double(),
## .. TotalE2012 = col_double(),
## .. TotalE2013 = col_double(),
## .. TotalE2014 = col_double(),
## .. TotalPrice2010 = col_double(),
## .. TotalPrice2011 = col_double(),
## .. TotalPrice2012 = col_double(),
## .. TotalPrice2013 = col_double(),
## .. TotalPrice2014 = col_double(),
## .. `TotalC10-11` = col_double(),
## .. `TotalC11-12` = col_double(),
## .. `TotalC12-13` = col_double(),
## .. `TotalC13-14` = col_double(),
## .. `TotalP10-11` = col_double(),
## .. `TotalP11-12` = col_double(),
## .. `TotalP12-13` = col_double(),
## .. `TotalP13-14` = col_double(),
## .. `TotalE10-11` = col_double(),
## .. `TotalE11-12` = col_double(),
## .. `TotalE12-13` = col_double(),
## .. `TotalE13-14` = col_double(),
## .. `TotalPrice10-11` = col_double(),
## .. `TotalPrice11-12` = col_double(),
## .. `TotalPrice12-13` = col_double(),
## .. `TotalPrice13-14` = col_double(),
## .. BiomassC2010 = col_double(),
## .. BiomassC2011 = col_double(),
## .. BiomassC2012 = col_double(),
## .. BiomassC2013 = col_double(),
## .. BiomassC2014 = col_double(),
## .. CoalC2010 = col_double(),
## .. CoalC2011 = col_double(),
## .. CoalC2012 = col_double(),
## .. CoalC2013 = col_double(),
## .. CoalC2014 = col_double(),
## .. CoalP2010 = col_double(),
## .. CoalP2011 = col_double(),
## .. CoalP2012 = col_double(),
## .. CoalP2013 = col_double(),
## .. CoalP2014 = col_double(),
## .. CoalE2010 = col_double(),
## .. CoalE2011 = col_double(),
## .. CoalE2012 = col_double(),
## .. CoalE2013 = col_double(),
## .. CoalE2014 = col_double(),
## .. CoalPrice2010 = col_double(),
## .. CoalPrice2011 = col_double(),
## .. CoalPrice2012 = col_double(),
## .. CoalPrice2013 = col_double(),
## .. CoalPrice2014 = col_double(),
## .. ElecC2010 = col_double(),
## .. ElecC2011 = col_double(),
## .. ElecC2012 = col_double(),
## .. ElecC2013 = col_double(),
## .. ElecC2014 = col_double(),
## .. ElecE2010 = col_double(),
## .. ElecE2011 = col_double(),
## .. ElecE2012 = col_double(),
## .. ElecE2013 = col_double(),
## .. ElecE2014 = col_double(),
## .. ElecPrice2010 = col_double(),
## .. ElecPrice2011 = col_double(),
## .. ElecPrice2012 = col_double(),
## .. ElecPrice2013 = col_double(),
## .. ElecPrice2014 = col_double(),
## .. FossFuelC2010 = col_double(),
## .. FossFuelC2011 = col_double(),
## .. FossFuelC2012 = col_double(),
## .. FossFuelC2013 = col_double(),
## .. FossFuelC2014 = col_double(),
## .. GeoC2010 = col_double(),
## .. GeoC2011 = col_double(),
## .. GeoC2012 = col_double(),
## .. GeoC2013 = col_double(),
## .. GeoC2014 = col_double(),
## .. GeoP2010 = col_double(),
## .. GeoP2011 = col_double(),
## .. GeoP2012 = col_double(),
## .. GeoP2013 = col_double(),
## .. GeoP2014 = col_double(),
## .. HydroC2010 = col_double(),
## .. HydroC2011 = col_double(),
## .. HydroC2012 = col_double(),
## .. HydroC2013 = col_double(),
## .. HydroC2014 = col_double(),
## .. HydroP2010 = col_double(),
## .. HydroP2011 = col_double(),
## .. HydroP2012 = col_double(),
## .. HydroP2013 = col_double(),
## .. HydroP2014 = col_double(),
## .. NatGasC2010 = col_double(),
## .. NatGasC2011 = col_double(),
## .. NatGasC2012 = col_double(),
## .. NatGasC2013 = col_double(),
## .. NatGasC2014 = col_double(),
## .. NatGasE2010 = col_double(),
## .. NatGasE2011 = col_double(),
## .. NatGasE2012 = col_double(),
## .. NatGasE2013 = col_double(),
## .. NatGasE2014 = col_double(),
## .. NatGasPrice2010 = col_double(),
## .. NatGasPrice2011 = col_double(),
## .. NatGasPrice2012 = col_double(),
## .. NatGasPrice2013 = col_double(),
## .. NatGasPrice2014 = col_double(),
## .. LPGC2010 = col_double(),
## .. LPGC2011 = col_double(),
## .. LPGC2012 = col_double(),
## .. LPGC2013 = col_double(),
## .. LPGC2014 = col_double(),
## .. LPGE2010 = col_double(),
## .. LPGE2011 = col_double(),
## .. LPGE2012 = col_double(),
## .. LPGE2013 = col_double(),
## .. LPGE2014 = col_double(),
## .. LPGPrice2010 = col_double(),
## .. LPGPrice2011 = col_double(),
## .. LPGPrice2012 = col_double(),
## .. LPGPrice2013 = col_double(),
## .. LPGPrice2014 = col_double(),
## .. GDP2010Q1 = col_double(),
## .. GDP2010Q2 = col_double(),
## .. GDP2010Q3 = col_double(),
## .. GDP2010Q4 = col_double(),
## .. GDP2010 = col_double(),
## .. GDP2011Q1 = col_double(),
## .. GDP2011Q2 = col_double(),
## .. GDP2011Q3 = col_double(),
## .. GDP2011Q4 = col_double(),
## .. GDP2011 = col_double(),
## .. GDP2012Q1 = col_double(),
## .. GDP2012Q2 = col_double(),
## .. GDP2012Q3 = col_double(),
## .. GDP2012Q4 = col_double(),
## .. GDP2012 = col_double(),
## .. GDP2013Q1 = col_double(),
## .. GDP2013Q2 = col_double(),
## .. GDP2013Q3 = col_double(),
## .. GDP2013Q4 = col_double(),
## .. GDP2013 = col_double(),
## .. GDP2014Q1 = col_double(),
## .. GDP2014Q2 = col_double(),
## .. GDP2014Q3 = col_double(),
## .. GDP2014Q4 = col_double(),
## .. GDP2014 = col_double(),
## .. CENSUS2010POP = col_double(),
## .. POPESTIMATE2010 = col_double(),
## .. POPESTIMATE2011 = col_double(),
## .. POPESTIMATE2012 = col_double(),
## .. POPESTIMATE2013 = col_double(),
## .. POPESTIMATE2014 = col_double(),
## .. RBIRTH2011 = col_double(),
## .. RBIRTH2012 = col_double(),
## .. RBIRTH2013 = col_double(),
## .. RBIRTH2014 = col_double(),
## .. RDEATH2011 = col_double(),
## .. RDEATH2012 = col_double(),
## .. RDEATH2013 = col_double(),
## .. RDEATH2014 = col_double(),
## .. RNATURALINC2011 = col_double(),
## .. RNATURALINC2012 = col_double(),
## .. RNATURALINC2013 = col_double(),
## .. RNATURALINC2014 = col_double(),
## .. RINTERNATIONALMIG2011 = col_double(),
## .. RINTERNATIONALMIG2012 = col_double(),
## .. RINTERNATIONALMIG2013 = col_double(),
## .. RINTERNATIONALMIG2014 = col_double(),
## .. RDOMESTICMIG2011 = col_double(),
## .. RDOMESTICMIG2012 = col_double(),
## .. RDOMESTICMIG2013 = col_double(),
## .. RDOMESTICMIG2014 = col_double(),
## .. RNETMIG2011 = col_double(),
## .. RNETMIG2012 = col_double(),
## .. RNETMIG2013 = col_double(),
## .. RNETMIG2014 = col_double()
## .. )
## - attr(*, "problems")=<externalptr>#remove NA
data=na.omit(data)#Filter data for ElecC
ElecC<-data%>%
select("StateCodes","State","Region","Division","Coast",`Great Lakes`,"ElecC2010","ElecC2011","ElecC2012","ElecC2013","ElecC2014")%>%
pivot_longer(c("ElecC2010","ElecC2011","ElecC2012","ElecC2013","ElecC2014"), names_to = "Year", values_to = "ElecConsumption")
#Clean Year ElecC
ElecC$Year<-gsub("ElecC","",as.character(ElecC$Year))
#Display the outcome
ElecC## # A tibble: 255 × 8
## StateCodes State Region Division Coast `Great Lakes` Year ElecConsumption
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 AL Alabama 3 6 1 0 2010 310023
## 2 AL Alabama 3 6 1 0 2011 303652
## 3 AL Alabama 3 6 1 0 2012 294055
## 4 AL Alabama 3 6 1 0 2013 299751
## 5 AL Alabama 3 6 1 0 2014 308765
## 6 AK Alaska 4 9 1 0 2010 21315
## 7 AK Alaska 4 9 1 0 2011 21562
## 8 AK Alaska 4 9 1 0 2012 21893
## 9 AK Alaska 4 9 1 0 2013 21387
## 10 AK Alaska 4 9 1 0 2014 21034
## # … with 245 more rows#Filter data for ElecE
ElecE<-data%>%
select("StateCodes","ElecE2010","ElecE2011","ElecE2012","ElecE2013","ElecE2014")%>%
pivot_longer(c("ElecE2010","ElecE2011","ElecE2012","ElecE2013","ElecE2014"), names_to = "Year", values_to = "ElecEarning")
#Clean Year ElecE
ElecE$Year<-gsub("ElecE","",as.character(ElecE$Year))
#Display the outcome
ElecE## # A tibble: 255 × 3
## StateCodes Year ElecEarning
## <chr> <chr> <dbl>
## 1 AL 2010 7833
## 2 AL 2011 7846.
## 3 AL 2012 7666.
## 4 AL 2013 7901.
## 5 AL 2014 8363.
## 6 AK 2010 912.
## 7 AK 2011 1005.
## 8 AK 2012 1035
## 9 AK 2013 1005.
## 10 AK 2014 1056.
## # … with 245 more rows#Filter data for ElecPrice
ElecPrice<-data%>%
select("StateCodes","ElecPrice2010","ElecPrice2011","ElecPrice2012","ElecPrice2013","ElecPrice2014")%>%
pivot_longer(c("ElecPrice2010","ElecPrice2011","ElecPrice2012","ElecPrice2013","ElecPrice2014"), names_to = "Year", values_to = "ElecPrice")
#Clean Year ElecPrice
ElecPrice$Year<-gsub("ElecPrice","",as.character(ElecPrice$Year))
#Display the outcome
ElecPrice## # A tibble: 255 × 3
## StateCodes Year ElecPrice
## <chr> <chr> <dbl>
## 1 AL 2010 26.4
## 2 AL 2011 27.1
## 3 AL 2012 27.3
## 4 AL 2013 26.5
## 5 AL 2014 27.2
## 6 AK 2010 43.3
## 7 AK 2011 47.1
## 8 AK 2012 47.8
## 9 AK 2013 48.4
## 10 AK 2014 51.3
## # … with 245 more rows#Join four Tables into one
Elec<-left_join(ElecC,ElecE,by=c('StateCodes','Year'))
Elec<-left_join(Elec,ElecPrice,by=c('StateCodes','Year'))
Elec<-Elec%>%
mutate(CumElecConsumption=cumsum(ElecConsumption))%>%
mutate(CumElecEarning=cumsum(ElecEarning))%>%
mutate(CumElecPrice=cumsum(ElecPrice))
Elec## # A tibble: 255 × 13
## StateCodes State Region Division Coast `Great Lakes` Year ElecConsumption
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <chr> <dbl>
## 1 AL Alabama 3 6 1 0 2010 310023
## 2 AL Alabama 3 6 1 0 2011 303652
## 3 AL Alabama 3 6 1 0 2012 294055
## 4 AL Alabama 3 6 1 0 2013 299751
## 5 AL Alabama 3 6 1 0 2014 308765
## 6 AK Alaska 4 9 1 0 2010 21315
## 7 AK Alaska 4 9 1 0 2011 21562
## 8 AK Alaska 4 9 1 0 2012 21893
## 9 AK Alaska 4 9 1 0 2013 21387
## 10 AK Alaska 4 9 1 0 2014 21034
## # … with 245 more rows, and 5 more variables: ElecEarning <dbl>,
## # ElecPrice <dbl>, CumElecConsumption <dbl>, CumElecEarning <dbl>,
## # CumElecPrice <dbl>Electrics Consumption Visulization
#Rank top 5 state of Elec Elec each year. (Bar plot)
#Overall
ElecCgeneral<-ggplot(data=Elec,aes(y=ElecConsumption,x=reorder(State,ElecConsumption)))+geom_col(aes(fill=State))+
scale_x_discrete(guide = guide_axis(check.overlap = TRUE))+
labs(
x="State",
y="ElecConsumption"
)+
theme(legend.position = 'none')
ElecCgeneral+facet_grid(Year ~.)
#Top 5 city in ElecC2010
ElecC2010<-Elec%>%
filter(Year==2010)%>%
arrange(desc(ElecConsumption))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecConsumption),y=ElecConsumption))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecConsumption"
)+
theme(legend.position = 'none')+
coord_flip()
ElecC2010
#Top 5 city in ElecC2011
ElecC2011<-Elec%>%
filter(Year==2011)%>%
arrange(desc(ElecConsumption))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecConsumption),y=ElecConsumption))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecConsumption"
)+
theme(legend.position = 'none')+
coord_flip()
ElecC2011
#Top 5 city in ElecC2012
ElecC2012<-Elec%>%
filter(Year==2012)%>%
arrange(desc(ElecConsumption))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecConsumption),y=ElecConsumption))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecConsumption"
)+
theme(legend.position = 'none')+
coord_flip()
ElecC2012
#Top 5 city in ElecC2013
ElecC2013<-Elec%>%
filter(Year==2013)%>%
arrange(desc(ElecConsumption))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecConsumption),y=ElecConsumption))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecConsumption"
)+
theme(legend.position = 'none')+
coord_flip()
ElecC2013
#Top 5 city in ElecC2014
ElecC2014<-Elec%>%
filter(Year==2014)%>%
arrange(desc(ElecConsumption))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecConsumption),y=ElecConsumption))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecConsumption"
)+
theme(legend.position = 'none')+
coord_flip()
ElecC2014
#Combine all together
ggarrange(ElecC2010,ElecC2010,ElecC2011,ElecC2012,ElecC2013,ElecC2014,nrow=2,ncol=3)
#Boxplot to show mean, median, min, max for each energy by coasts annually
#General
ElecCboxgeneral<-ggplot(Elec,aes(x=Year,y=ElecConsumption,fill=Year))+geom_boxplot(outlier.colour="red", outlier.shape=8, outlier.size=4) +
geom_jitter()
ElecCboxgeneral
#2010
ElecCbox2010<-Elec%>%
filter(Year==2010)%>%
ggplot(aes(x=factor(Region),y=ElecConsumption,fill=factor(Region)))+geom_violin()
ElecCbox2010
#2011
ElecCbox2011<-Elec%>%
filter(Year==2011)%>%
ggplot(aes(x=factor(Region),y=ElecConsumption,fill=factor(Region)))+geom_violin()
ElecCbox2011
#2012
ElecCbox2012<-Elec%>%
filter(Year==2012)%>%
ggplot(aes(x=factor(Region),y=ElecConsumption,fill=factor(Region)))+geom_violin()
ElecCbox2012
#2013
ElecCbox2013<-Elec%>%
filter(Year==2013)%>%
ggplot(aes(x=factor(Region),y=ElecConsumption,fill=factor(Region)))+geom_violin()
ElecCbox2013
#2014
ElecCbox2014<-Elec%>%
filter(Year==2014)%>%
ggplot(aes(x=factor(Region),y=ElecConsumption,fill=factor(Region)))+geom_violin()
ElecCbox2014
#Combine all together
ggarrange(ElecCboxgeneral,ElecCbox2010,ElecCbox2011,ElecCbox2012,ElecCbox2013,ElecCbox2014,nrow=2,ncol=3)
#Time series for Elec Consumption
Elec$Year=as.numeric(Elec$Year)
ggplot(Elec,aes(x = Year,y = CumElecConsumption)) +geom_point(aes(color=factor(State))) +geom_line(aes(color=factor(State))) 
# Elec Earning Visulization
#Rank top 5 state of Elec production each year. (Bar plot)
#Overall
ElecEgeneral<-ggplot(data=Elec,aes(y=ElecEarning,x=reorder(State,ElecEarning)))+geom_col(aes(fill=State))+
scale_x_discrete(guide = guide_axis(check.overlap = TRUE))+
labs(
x="State",
y="ElecEarning"
)+
theme(legend.position = 'none')
ElecEgeneral+facet_grid(Year ~.)
#Top 5 city in ElecP2010
ElecE2010<-Elec%>%
filter(Year==2010)%>%
arrange(desc(ElecEarning))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecEarning),y=ElecEarning))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecEarning"
)+
theme(legend.position = 'none')+
coord_flip()
ElecE2010
#Top 5 city in Elec2011
ElecE2011<-Elec%>%
filter(Year==2011)%>%
arrange(desc(ElecEarning))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecEarning),y=ElecEarning))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecEarning"
)+
theme(legend.position = 'none')+
coord_flip()
ElecE2011
#Top 5 city in Elec2012
ElecE2012<-Elec%>%
filter(Year==2012)%>%
arrange(desc(ElecEarning))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecEarning),y=ElecEarning))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecEarning"
)+
theme(legend.position = 'none')+
coord_flip()
ElecE2012
#Top 5 city in Elec2013
ElecE2013<-Elec%>%
filter(Year==2013)%>%
arrange(desc(ElecEarning))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecEarning),y=ElecEarning))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecEarning"
)+
theme(legend.position = 'none')+
coord_flip()
ElecE2013
#Top 5 city in Elec2014
ElecE2014<-Elec%>%
filter(Year==2014)%>%
arrange(desc(ElecEarning))%>%
slice(1:5)%>%
ggplot(aes(x=reorder(State,ElecEarning),y=ElecEarning))+geom_col(aes(fill=State))+
labs(
x="State",
y="ElecEarning"
)+
theme(legend.position = 'none')+
coord_flip()
ElecE2014
#Combine all together
ggarrange(ElecEgeneral,ElecE2010,ElecE2011,ElecE2012,ElecE2013,ElecE2014,nrow=2,ncol=3)
#Time series for Elec Earning
Elec$Year=as.numeric(Elec$Year)
ggplot(Elec,aes(x = Year,y = CumElecEarning)) +geom_point(aes(color=factor(State))) +geom_line(aes(color=factor(State))) 
#Caol Price
#Boxplot to show mean, median, min, max for each energy by coasts annually
Elec$Year=as.character(Elec$Year)
#General
ElecPriceboxgeneral<-ggplot(Elec,aes(x=Year,y=ElecPrice,fill=Year))+geom_boxplot(outlier.colour="red", outlier.shape=8, outlier.size=4) +
geom_jitter()
ElecPriceboxgeneral
#2010
ElecPricebox2010<-Elec%>%
filter(Year==2010)%>%
ggplot(aes(x=factor(Region),y=ElecPrice,fill=factor(Region)))+geom_violin()
ElecPricebox2010
#2011
ElecPricebox2011<-Elec%>%
filter(Year==2011)%>%
ggplot(aes(x=factor(Region),y=ElecPrice,fill=factor(Region)))+geom_violin()
ElecPricebox2011
#2012
ElecPricebox2012<-Elec%>%
filter(Year==2012)%>%
ggplot(aes(x=factor(Region),y=ElecPrice,fill=factor(Region)))+geom_violin()
ElecPricebox2012
#2013
ElecPricebox2013<-Elec%>%
filter(Year==2013)%>%
ggplot(aes(x=factor(Region),y=ElecPrice,fill=factor(Region)))+geom_violin()
ElecPricebox2013
#2014
ElecPricebox2014<-Elec%>%
filter(Year==2014)%>%
ggplot(aes(x=factor(Region),y=ElecPrice,fill=factor(Region)))+geom_violin()
ElecPricebox2014
#Combine all together
ggarrange(ElecPriceboxgeneral,ElecPricebox2010,ElecPricebox2011,ElecPricebox2012,ElecPricebox2013,ElecPricebox2014,nrow=2,ncol=3)
#Time series for Elec Consumption
Elec$Year=as.numeric(Elec$Year)
ggplot(Elec,aes(x = Year,y = CumElecPrice)) +geom_point(aes(color=factor(State))) +geom_line(aes(color=factor(State))) 